Search Results for author:
Found 20 papers, 16 papers with code
ProSpect: Expanded Conditioning for the Personalization of Attribute-aware Image Generation
Personalizing generative models offers a way to guide image generation with user-provided references.
Progressive Open Space Expansion for Open-Set Model Attribution
In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones.
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Our framework consists of three key components, i. e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
Region-Aware Diffusion for Zero-shot Text-driven Image Editing
Different from current mask-based image editing methods, we propose a novel region-aware diffusion model (RDM) for entity-level image editing, which could automatically locate the region of interest and replace it following given text prompts.
Inversion-Based Style Transfer with Diffusion Models
Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions.
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into the stylized one according to textual descriptions of the target style provided by the user.
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided Diffusion
Extensive experimental results on the quality and quantity of the generated digital art paintings confirm the effectiveness of the combination of the diffusion model and multimodal guidance.
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Our framework consists of three key components, i. e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
SIOD: Single Instance Annotated Per Category Per Image for Object Detection
Object detection under imperfect data receives great attention recently.
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection
Semi-supervised object detection has recently achieved substantial progress.
StyTr2: Image Style Transfer With Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.
StyTr$^2$: Image Style Transfer with Transformers
The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content.
Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network.
Arbitrary Video Style Transfer via Multi-Channel Correlation
Towards this end, we propose Multi-Channel Correction network (MCCNet), which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.
Distribution Aligned Multimodal and Multi-Domain Image Stylization
Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously.
Arbitrary Style Transfer via Multi-Adaptation Network
Arbitrary style transfer is a significant topic with research value and application prospect.
Multi-Attribute Guided Painting Generation
Controllable painting generation plays a pivotal role in image stylization.
Image Retargetability
In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting.
